Universität zu Köln

Pironti, Alejandro, Pfeifer, Nico, Walter, Hauke, Jensen, Bjorn-Erik O., Zazzi, Maurizio ORCID: 0000-0002-0344-6281, Gomes, Perpetua ORCID: 0000-0003-3271-8255, Kaiser, Rolf and Lengauer, Thomas ORCID: 0000-0003-3801-2640 (2017). Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool. PLoS One, 12 (4). SAN FRANCISCO: PUBLIC LIBRARY SCIENCE. ISSN 1932-6203

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Abstract

Antiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines often attending different clinics. We trained statistical models for predicting drug exposure from current HIV-1 genotype. These models were trained on 63,742 HIV-1 nucleotide sequences derived from patients with known therapeutic history, and on 6,836 genotype-phenotype pairs (GPPs). The mean performance regarding prediction of drug exposure on two test sets was 0.78 and 0.76 (ROC-AUC), respectively. The mean correlation to phenotypic resistance in GPPs was 0.51 (PhenoSense) and 0.46 (Antivirogram). Performance on prediction of therapy-success on two test sets based on genetic susceptibility scores was 0.71 and 0.63 (ROC-AUC), respectively. Compared to geno2pheno[resistance], our novel models display a similar or superior performance. Our models are freely available on the internet via www.geno2pheno.org. They can be used for inferring which drug compounds have previously been used by an HIV-1-infected patient, for predicting drug resistance, and for selecting an optimal antiretroviral therapy. Our data-driven models can be periodically retrained without expert intervention as clinical HIV-1 databases are updated and therefore reduce our dependency on hard-to-obtain GPPs.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Pironti, Alejandro UNSPECIFIED UNSPECIFIED UNSPECIFIED Pfeifer, Nico UNSPECIFIED UNSPECIFIED UNSPECIFIED Walter, Hauke UNSPECIFIED UNSPECIFIED UNSPECIFIED Jensen, Bjorn-Erik O. UNSPECIFIED UNSPECIFIED UNSPECIFIED Zazzi, Maurizio UNSPECIFIED orcid.org/0000-0002-0344-6281 UNSPECIFIED Gomes, Perpetua UNSPECIFIED orcid.org/0000-0003-3271-8255 UNSPECIFIED Kaiser, Rolf UNSPECIFIED UNSPECIFIED UNSPECIFIED Lengauer, Thomas UNSPECIFIED orcid.org/0000-0003-3801-2640 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-234019
DOI:	10.1371/journal.pone.0174992
Journal or Publication Title:	PLoS One
Volume:	12
Number:	4
Date:	2017
Publisher:	PUBLIC LIBRARY SCIENCE
Place of Publication:	SAN FRANCISCO
ISSN:	1932-6203
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Mathematics and Computer Science > Institute of Computer Science
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language IMMUNODEFICIENCY-VIRUS TYPE-1; ANTIRETROVIRAL THERAPY; REVERSE-TRANSCRIPTASE; COMPUTATIONAL MODELS; PHENOTYPIC RESISTANCE; HIV-INFECTION; INHIBITORS; MUTATIONS; PROTEASE; RECOMMENDATIONS Multiple languages Multidisciplinary Sciences Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/23401